| title | author | date | output |
|---|---|---|---|
Postdoc project 1.1.1.2/VIAA/1/16/112 |
Dmitry Pavlyuk |
Oct 1 2017 - Sep 30, 2020 |
html_document |
This repository supports researches executed within the scope of Dmitry Pavlyuk's postdoctoral research project No. 1.1.1.2/VIAA/1/16/112 "Spatiotemporal urban traffic modelling using big data"
The main purpose of the repository is to ensure robust and reproducable research results.
Mainly in this project we use data in ADUS (.traffic) format. The Archived Data User Service (ADUS) provides the U.S. National ITS Architecture with the requirements for archiving and re-use of data collected for ITS operations. Several popular research data providers utilise ADUS, e.g. California Freeway Performance Measurement Project (PeMS), Minnesota - Traffic Management Center, etc.
A usual study includes following data processing steps:
- Data collection. We assume that data is publicly available and can be downloaded (like ADUS data).
See data collection markdown for download routines. - Data transformation. Collected data are trnasformed into convenietn format. See data transformation markdown for transformation routines.
- Data sampling and cleaning. See data sampling and cleaning markdown for transformation routines.
- Conference- or article-specific markdowns
- 6th International Conference on Models and Technologies for Intelligent Transportation Systems - MT-ITS 2019 markdown
- "Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting", published in Alogrithms https://www.mdpi.com/1999-4893/13/2/39 markdown
- 22nd Euro Working Group on Transportation Meeting - EWGT 2019 markdown https://ewgt19.upc.edu/en
- 6th International conference on Time Series and Forecasting - ITISE 2019 markdown http://itise.ugr.es/
- 19th International Multi-Conference Reliability and Statistics in Transportation and Communication - RelStat-2019 markdown http://relstat2019.tsi.lv/
- Transport Research Area 2020 markdown with further improvements for publication in Transport journal
- "Robust Learning of Spatiotemporal Urban Traffic Flow Relationships" prepared for submission markdown
- 23nd Euro Working Group on Transportation Meeting - EWGT 2020 markdown http://www.ewgt2020.eu/